Module: TrackObjects

When trying to track an object in an image, TrackObjects will search within a maximum specified distance (see the distance within which to search setting) of the object's location in the previous image, looking for a "match". Objects that match are assigned the same number, or label, throughout the entire movie. There are several options for the method used to find a match. Choose among these options based on which is most consistent from frame to frame of your movie.

• Overlap: Compares the amount of spatial overlap between identified objects in the previous frame with those in the current frame. The object with the greatest amount of spatial overlap will be assigned the same number (label). Recommended when there is a high degree of overlap of an object from one frame to the next, which is the case for movies with high frame rates relative to object motion.
• Distance: Compares the distance between each identified object in the previous frame with that of the current frame. The closest objects to each other will be assigned the same number (label). Distances are measured from the perimeter of each object. Recommended for cases where the objects are not very crowded but where Overlap does not work sufficiently well, which is the case for movies with low frame rates relative to object motion.
• Measurements: Compares each object in the current frame with objects in the previous frame based on a particular feature you have measured for the objects (for example, a particular intensity or shape measurement that can distinguish nearby objects). The object with the closest-matching measurement will be selected as a match and will be assigned the same number (label). This selection requires that you run the specified Measure module previous to this module in the pipeline so that the measurement values can be used to track the objects.
• LAP: Uses the linear assignment problem (LAP) framework. The linear assignment problem (LAP) algorithm (Jaqaman et al., 2008) addresses the challenges of high object density, motion heterogeneity, temporary disappearances, and object merging and splitting. The algorithm first links objects between consecutive frames and then links the resulting partial trajectories into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of object trajectories throughout a movie. Tracks are constructed from an image sequence by detecting objects in each frame and linking objects between consecutive frames as a first step. This step alone may result in incompletely tracked objects due to the appearance and disappearance of objects, either in reality or apparently because of noise and imaging limitations. To correct this, you may apply an optional second step which closes temporal gaps between tracked objects and captures merging and splitting events. This step takes place at the end of the analysis run. References
  • Jaqaman K, Loerke D, Mettlen M, Kuwata H, Grinstein S, Schmid SL, Danuser G. (2008) "Robust single-particle tracking in live-cell time-lapse sequences." Nature Methods 5(8),695-702. (link)
  • Jaqaman K, Danuser G. (2009) "Computational image analysis of cellular dynamics: a case study based on particle tracking." Cold Spring Harb Protoc. 2009(12):pdb.top65. (link)

Select the objects to be tracked by this module.

(Used only if Measurements is the tracking method)
Select which type of measurement (category) and which specific feature from the Measure module will be used for tracking. Select the feature name from the popup box or see each Measure module's help for the list of the features measured by that module. If necessary, you will also be asked to specify additional details such as the image from which the measurements originated or the measurement scale.

Objects in the subsequent frame will be considered potential matches if they are within this distance. To determine a suitable pixel distance, you can look at the axis increments on each image (shown in pixel units) or use the distance measurement tool. To measure distances in an open image, use the "Measure length" tool under Tools in the display window menu bar. If you click on an image and drag, a line will appear between the two endpoints, and the distance between them shown at the right-most portion of the bottom panel.

The output image can be saved as:

• Color Only: A color-labeled image, with each tracked object assigned a unique color
• Color and Number: Same as above but with the tracked object number superimposed.

Select Yes to retain the image showing the tracked objects for later use in the pipeline. For example, a common use is for quality control purposes saving the image with the SaveImages module.

Please note that if you are using the second phase of the LAP method, the final labels are not assigned until after the pipeline has completed the analysis run. That means that saving the color-coded image will only show the penultimate result and not the final product.

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(Used only if saving the color-coded image)
Enter a name to give the color-coded image of tracked labels.

(Used only if the LAP tracking method is applied)
This setting controls how to predict an object's position in the next frame, assuming that each object moves randomly with a frame-to-frame variance in position that follows a Gaussian distribution.

• Random: A model in which objects move due to Brownian Motion or a similar process where the variance in position differs between objects. Use this model if the objects move with some random jitter around a stationary location.
• Velocity: A model in which the object moves with a velocity. Both velocity and position (after correcting for velocity) vary following a Gaussian distribution. Use this model if the objects move along a spatial trajectory in some direction over time.
• Both: TrackObjects will predict each object's position using both models and use the model with the lowest penalty to join an object in one frame with one in another. Use this option if both models above are applicable over time.

(Used only if the LAP tracking method is applied)
TrackObjects will estimate the variance of the error between the observed and predicted positions of an object for each movement model. It will constrain the search for matching objects from one frame to the next to the standard deviation of the error times the number of standard deviations that you enter here.

(Used only if the LAP tracking method is applied)
Care must be taken to adjust the upper limit appropriate to the data.
TrackObjects derives a search radius based on the error estimation. Potentially, the module can make an erroneous assignment with a large error, leading to a large estimated error for the object in the next frame. Conversely, the module can arrive at a small estimated error by chance, leading to a maximum radius that does not track the object in a subsequent frame. The radius limit constrains the maximum radius to reasonable values.

The lower limit should be set to a radius (in pixels) that is a reasonable displacement for any object from one frame to the next. The upper limit should be set to the maximum reasonable displacement under any circumstances.

(Used only if the LAP tracking method is applied)
Select Yes to run the second phase of the LAP algorithm after processing all images. Select No to omit the second phase or to perform the second phase when running the module as a data tool.

Since object tracks may start and end not only because of the true appearance and disappearance of objects, but also because of apparent disappearances due to noise and limitations in imaging, you may want to run the second phase which attempts to close temporal gaps between tracked objects and tries to capture merging and splitting events.

For additional details on optimizing the LAP settings, refer to Jaqaman K, Danuser G. "Computational image analysis of cellular dynamics: a case study based on particle tracking." Cold Spring Harb Protocols 2009(12) (link), in particular the section "Adjustment of control parameters and diagnostics for track evaluation."

(Used only if the LAP tracking method is applied and the second phase is run)
This setting assigns a cost to keeping a gap caused when an object is missing from one of the frames of a track (the alternative to keeping the gap is to bridge it by connecting the tracks on either side of the missing frames). The cost of bridging a gap is the distance, in pixels, of the displacement of the object between frames.

Recommendations:

• Set the gap cost higher if tracks from objects in previous frames are being erroneously joined, across a gap, to tracks from objects in subsequent frames.
• Set the cost lower if tracks are not properly joined due to gaps caused by mis-segmentation.

(Used only if the LAP tracking method is applied and the second phase is run)
This setting is the cost of keeping two tracks distinct when the alternative is to make them into one track that splits. A split occurs when an object in one frame is assigned to the same track as two objects in a subsequent frame. The split cost takes two components into account:

• The area of the split object relative to the area of the resulting objects.
• The displacement of the resulting objects relative to the position of the original object.

The split cost is roughly measured in pixels. The split alternative cost is (conceptually) subtracted from the cost of making the split.

Recommendations:

• The split cost should be set lower if objects are being split that should not be split.
• The split cost should be set higher if objects that should be split are not.

(Used only if the LAP tracking method is applied and the second phase is run)
This setting is the cost of keeping two tracks distinct when the alternative is to merge them into one. A merge occurs when two objects in one frame are assigned to the same track as a single object in a subsequent frame. The merge score takes two components into account:

• The area of the two objects to be merged relative to the area of the resulting objects.
• The displacement of the original objects relative to the final object.

The merge cost is measured in pixels. The merge alternative cost is (conceptually) subtracted from the cost of making the merge.

Recommendations:

• Set the merge alternative cost lower if objects are being merged when they should otherwise be kept separate.
• Set the merge alternative cost higher if objects that are not merged should be merged.

(Used only if the LAP tracking method is applied and the second phase is run)
This setting acts as a filter for unreasonably large displacements during the second phase.

Recommendations:

• The maximum gap displacement should be set to roughly the maximum displacement of an object's center from frame to frame. An object that makes large frame-to-frame jumps should have a higher value for this setting than one that only moves slightly.
• Be aware that the LAP algorithm will run more slowly with a higher maximum gap displacement value, since the higher this value, the more objects that must be compared at each step.
• Objects that would have been tracked between successive frames for a lower maximum displacement may not be tracked if the value is set higher.

(Used only if the LAP tracking method is applied and the second phase is run)
This setting acts as a filter for unreasonably large split scores. The split score has two components:

• The area of the initial object relative to the area of the two objects resulting from the split.
• The distances between the original and resulting objects.

Recommendations:

• The LAP algorithm will run more slowly with a maximum split score value.
• Objects that would have been split at a lower maximum split score will not be considered for splitting.

(Used only if the LAP tracking method is applied and the second phase is run)
This setting acts as a filter for unreasonably large merge scores. The merge score has two components:

• The area of the resulting merged object relative to the area of the two objects to be merged.
• The distances between the objects to be merged and the resulting object.

Recommendations:

• The LAP algorithm will run more slowly with a higher maximum merge score value.
• Objects that would have been merged at a lower maximum merge score will not be considered for merging.

(Used only if the LAP tracking method is applied and the second phase is run)
Care must be taken to adjust this setting appropriate to the data.
This setting controls the maximum number of frames that can be skipped when merging a gap caused by an unsegmented object. These gaps occur when an image is mis-segmented and identification fails to find an object in one or more frames.

Recommendations:

• Set the maximum gap higher in order to have more chance of correctly recapturing an object after erroneously losing the original for a few frames.
• Set the maximum gap lower to reduce the chance of erroneously connecting to the wrong object after correctly losing the original object (e.g., if the cell dies or moves off-screen).

Select Yes if you want objects to be filtered by their lifetime, i.e., total duration in frames. This is useful for marking objects which transiently appear and disappear, such as the results of a mis-segmentation.

Recommendations:

• This operation does not actually delete the filtered object, but merely removes its label from the tracked object list; the filtered object's per-object measurements are retained.
• An object can be filtered only if it is tracked as an unique object. Splits continue the lifetime count from their parents, so the minimum lifetime value does not apply to them.

(Used only if objects are filtered by lifetime)
Select Yes to filter the object on the basis of a minimum number of frames.

Enter the minimum number of frames an object is permitted to persist. Objects which last this number of frames or lower are filtered out.

(Used only if objects are filtered by lifetime)
Select Yes to filter the object on the basis of a maximum number of frames.

Enter the maximum number of frames an object is permitted to persist. Objects which last this number of frames or more are filtered out.